java将一段英文短文中各字符（包括标点符号）的统计出的频率值，自动生成01哈夫曼编码，并将该短文编码成01编码；

以下是Java代码示例，可以实现将输入的英文短文进行哈夫曼编码处理，并输出编码后的01序列：

import java.util.PriorityQueue;
import java.util.Scanner;

class Node implements Comparable<Node> {
    char ch;
    int freq;
    Node left, right;

    Node(char ch, int freq, Node left, Node right) {
        this.ch = ch;
        this.freq = freq;
        this.left = left;
        this.right = right;
    }

    public int compareTo(Node other) {
        return this.freq - other.freq;
    }

    public boolean isLeaf() {
        return left == null && right == null;
    }
}

public class HuffmanEncoder {
    private String[] codeTable = new String[256];
    private Node root;

    public void encode(String text) {
        int[] freq = new int[256];
        for (int i = 0; i < text.length(); i++) {
            freq[text.charAt(i)]++;
        }

        PriorityQueue<Node> pq = new PriorityQueue<>();
        for (int i = 0; i < 256; i++) {
            if (freq[i] > 0) {
                pq.offer(new Node((char) i, freq[i], null, null));
            }
        }

        while (pq.size() > 1) {
            Node left = pq.poll();
            Node right = pq.poll();
            Node parent = new Node('\0', left.freq + right.freq, left, right);
            pq.offer(parent);
        }

        root = pq.poll();
        createCodeTable(root, "");
        StringBuilder sb = new StringBuilder();
        for (int i = 0; i < text.length(); i++) {
            sb.append(codeTable[text.charAt(i)]);
        }
        System.out.println("Encoded text: " + sb.toString());
    }

    private void createCodeTable(Node node, String code) {
        if (node.isLeaf()) {
            codeTable[node.ch] = code;
            return;
        }
        createCodeTable(node.left, code + "0");
        createCodeTable(node.right, code + "1");
    }

    public static void main(String[] args) {
        Scanner sc = new Scanner(System.in);
        System.out.println("Enter the text to encode:");
        String text = sc.nextLine();
        HuffmanEncoder encoder = new HuffmanEncoder();
        encoder.encode(text);
    }
}

代码中使用了Node类来存储每个字符及其出现次数，并使用PriorityQueue来维护节点，以便于构建哈夫曼树。在构建哈夫曼树的过程中，使用了贪心策略，每次选取频率最小的两个节点进行合并。构建完成后，使用递归的方式生成每个字符的编码，并将编码存储在codeTable数组中。最后，使用编码表将原始文本编码成01序列，并输出结果。